List Affitor Skill

Turn a repeatable AI prompt or workflow into a structured, shareable skill for the affiliate-skills GitHub repository. The output is a complete SKILL.md file that works in any AI agent — shared via npx skills add Affitor/affiliate-skills so anyone can install it.

Stage

This skill belongs to Stage S8: Meta

When to Use

• User has a prompt they keep reusing and wants to turn it into a shareable skill
• User wants to create a new skill for the affiliate-skills repository
• User wants to write a SKILL.md file in the standard format
• User says "make this a skill" or "write a skill for X"
• User wants to package an AI workflow so others can replicate it

Input Schema

{
  raw_prompt: string       # (required) The prompt, workflow description, or detailed explanation of what the skill does
  failure_modes: string    # (optional) What goes wrong when the output is bad — helps write better Instructions and Error Handling
  niche: string            # (optional) Category hint, e.g., "content", "research", "seo"
  examples: string         # (optional) Example input/output pairs the user already has
}

Workflow

Step 1: Understand What the Prompt Actually Does

Before writing anything, analyze the user's raw prompt or workflow description:

1. Task type — Is this content creation, research, analysis, planning, automation, or something else?
2. Variable inputs — What changes each time? (product name, URL, audience, topic, etc.)
3. Fixed structure — What stays the same? (output format, sections, tone, constraints)
4. Quality differentiator — What makes a good output vs. a bad one?
5. Failure modes — Where does the AI tend to go wrong without explicit guidance?

If the user gave a vague description instead of an actual prompt, ask:

• "What do you typically paste into ChatGPT/Claude for this?"
• "What does the output look like when it works well?"
• "What goes wrong when it doesn't?"

If the user says "just do it", infer from context and proceed.

Step 2: Determine Skill Metadata

Based on the analysis, determine:

Step 3: Write the SKILL.md

Create a complete SKILL.md following this exact structure. Every section is required.

Frontmatter (YAML)

---
name: [slug]
description: >
  [2-3 lines. First line: what it does. Second line: trigger phrases.
  This is used for skill discovery — be specific about use cases.]
license: MIT
version: "1.0.0"
tags: [relevant tags]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: [user handle or "affitor"]
  version: "1.0"
  stage: [S1-S8]
---

Title and Introduction One paragraph. What the skill does and what makes the output reliable. No marketing speak.

When to Use 3-5 specific trigger scenarios. "Writing a blog post" is too vague. "You need to publish a comparison post for two competing SaaS tools this week" is useful.

Input Schema Typed definition of every variable input. Mark required vs optional.

Workflow (numbered steps) This is the core. Each step must be concrete enough that any AI model produces consistent output:

• Action — what to do
• Approach — how to do it specifically
• Quality bar — what good looks like

Bad: "3. Write the pros and cons" Good: "3. Write at least 3 pros and 2 cons. Each must reference a specific feature, not a vague category. 'Exports to 12 formats including PDF and DOCX' not 'Great export options'."

Output Schema Typed fields that other skills can consume via conversation context. Include output_schema_version: "1.0.0".

Output Format A markdown code block showing the exact template with [placeholder] brackets. This is the single most important section for consistency.

Error Handling 3-5 named failure modes with specific recovery behavior. What happens when input is missing, ambiguous, or the task can't be completed?

Examples 2-3 concrete examples showing:

• User input
• Key decisions made during the workflow
• What the output looks like (excerpt, not full)

Flywheel Connections

• Feeds Into: which skills consume this skill's output
• Fed By: which skills produce input for this skill
• Feedback Loop: how community engagement improves the skill
• chain_metadata YAML block with skill_slug, stage, timestamp, suggested_next

Quality Gate 5-7 numbered checklist items that must all pass before the output is delivered. These are the self-validation checks the AI runs silently.

References Links to supplementary reference files if applicable.

Step 4: Write the README Description

Separately from the SKILL.md, write a community-facing description for the skill's README section (for GitHub and the affiliate-skills registry). This is what people see when browsing — it sells the skill, not documents it.

Structure:

1. Opening (2 sentences) — what the skill does, who it's for
2. When to Use (3 bullets) — specific scenarios
3. What Makes It Different (brief) — why this skill vs. just prompting
4. Instructions summary — condensed version of the workflow
5. Input Required — what the user needs to provide
6. Output Format — what the skill produces (show template)
7. Example — one concrete input/output
8. Tips (3-5) — practical advice for getting the best results

This is NOT the SKILL.md content — it's a human-friendly summary for discovery.

Step 5: Assemble Output

Present two clearly separated outputs:

1. SKILL.md — the full file, ready to save to skills/{stage}/{slug}/SKILL.md
2. README Description — community-facing description for the GitHub repo and registry

Step 6: Self-Validation

Before presenting output, verify:

• SKILL.md has all required sections (frontmatter, intro, when-to-use, input schema, workflow, output schema, output format, error handling, examples, flywheel, quality gate)
• Every workflow step has action + approach + quality bar
• Output Format uses a code block with [placeholder] brackets
• At least 2 examples with concrete input/output
• Error handling covers realistic failure modes, not hypothetical ones
• Quality gate items are testable (not "make sure it's good")
• Description is specific enough that someone knows if it's relevant before clicking
• Frontmatter name matches the slug exactly

Output Schema

Other skills consume these fields from conversation context:

{
  output_schema_version: "1.0.0"
  skill_md: string           # Complete SKILL.md file content (ready to write to disk)
  listing: {
    name: string             # "Comparison Post Writer"
    slug: string             # "comparison-post-writer"
    description: string      # Community-facing description for GitHub README / registry
    content: string          # Full SKILL.md content (for the content field)
    category: string         # "content", "research", "seo", etc.
    level: string            # "beginner", "intermediate", "advanced"
    tags: string[]           # ["content", "comparison", "seo", "blog"]
  }
  metadata: {
    stage: string            # "S2-Content"
    tools_needed: string[]   # ["web_search"] or []
    estimated_time: string   # "15 min"
  }
}

Output Format

The skill produces two outputs:

Output 1: SKILL.md File

---
name: [slug]
description: >
  [2-3 lines describing the skill and trigger phrases]
license: MIT
version: "1.0.0"
tags: [[tags]]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: [author]
  version: "1.0"
  stage: [S1-S8 stage]
---

# [Skill Name]

[1 paragraph intro]

## Stage

This skill belongs to Stage [S1-S8]: [Stage Name]

## When to Use

- [Scenario 1]
- [Scenario 2]
- [Scenario 3]

## Input Schema

[typed input definition]

## Workflow

### Step 1: [Action]
[Instructions with approach and quality bar]

### Step 2: [Action]
[Instructions]

...

## Output Schema

[typed output definition with output_schema_version]

## Output Format

[code block template with [placeholders]]

## Error Handling

- **[Failure mode 1]:** [Recovery behavior]
- **[Failure mode 2]:** [Recovery behavior]

## Examples

**Example 1: [Scenario]**
[Input, decisions, output excerpt]

**Example 2: [Scenario]**
[Input, decisions, output excerpt]

## Flywheel Connections

### Feeds Into
- [skill] ([stage]) — [how]

### Fed By
- [skill] ([stage]) — [how]

### Feedback Loop
[How community engagement improves this skill]

chain_metadata YAML block

## Quality Gate

1. [Testable check]
2. [Testable check]
...

## References

- [reference files if applicable]

Output 2: README / Registry Description

## Skill Info (for GitHub README and registry)

| Field | Value |
|-------|-------|
| Name | [Skill Name] |
| Slug | [slug] |
| Category | [category] |
| Level | [level] |
| Tags | [tag1, tag2, tag3] |
| Install | npx skills add Affitor/affiliate-skills |

---

## Description (paste into README or PR description)

[Community-facing description — see Step 4]

Error Handling

• User gives a vague description instead of a prompt: Ask for the actual prompt they paste into AI, or a concrete example of input and expected output. If they say "just figure it out", do your best but flag that the skill may need iteration.
• Prompt is too simple for a skill: If the prompt is a single sentence with no variable inputs (e.g., "write me a joke"), tell the user this doesn't need to be a skill — it's already a prompt. Skills add value when there are variable inputs, structured outputs, and quality concerns.
• Prompt does too many things: If the workflow has 10+ distinct steps covering different domains, suggest splitting into 2-3 focused skills that chain together via flywheel connections.
• No clear output format: If the user can't describe what good output looks like, ask for 1-2 examples. Build the Output Format section from those examples.
• User wants to copy an existing skill: Check if a similar skill already exists in the affiliate-skills repo. If so, suggest improving the existing one rather than creating a duplicate.

Examples

Example 1: Converting a reused ChatGPT prompt

User: "I always ask ChatGPT to write me a comparison blog post about two SaaS tools for my affiliate site. Sometimes it's great, sometimes it writes a generic listicle."

Analysis:

• Task: content creation (comparison post)
• Variable inputs: product A, product B, niche
• Fixed structure: comparison table, pros/cons, recommendation with affiliate links
• Failure mode: generic listicle instead of specific comparison
• Stage: S3-Blog, Category: seo

Output: SKILL.md with comparison-post-writer slug, detailed workflow for researching both products, building comparison tables, writing specific pros/cons, and a quality gate that checks for concrete feature comparisons (not generic praise).

Example 2: Packaging a workflow that spans multiple tools

User: "I have a workflow where I research a product on G2, check their pricing page, then write a review post with SEO keywords. Can you make this a skill?"

Analysis:

• Task: research + content creation (review post)
• Variable inputs: product name, target keywords
• Tools needed: web_search, web_fetch
• Fixed structure: G2 data extraction, pricing analysis, SEO-optimized review
• Stage: S3-Blog, Category: seo, Level: intermediate

Output: SKILL.md with product-review-writer slug, workflow that explicitly separates research (Steps 1-3) from writing (Steps 4-6), output schema that passes review data to downstream skills, and error handling for when G2 listings don't exist.

Example 3: Skill that's too simple

User: "Make a skill that writes Twitter threads"

Response: Ask clarifying questions — "What kind of Twitter threads? About a product you're promoting? Summarizing a blog post? Sharing tips in a niche? The skill needs a specific use case to produce consistent output. 'Write a Twitter thread' is too broad — the AI will produce different things every time."

Revenue & Action Plan

Expected Outcomes

• Authority potential: Published skills build your authority as an affiliate marketing expert. Each skill in the affiliate-skills repo acts as a lead magnet — users install it, star the repo, and discover your work. Top skill contributors see compounding GitHub traffic and community recognition
• Benchmark: Skills used in real workflows get referenced, forked, and improved by the community. The first few installs are the hardest — after that, word-of-mouth takes over
• Key metric to track: GitHub stars + installs via npx skills add Affitor/affiliate-skills. Secondary: community discussions, PRs, and mentions

Do This Right Now (15 min)

1. Submit the skill via GitHub PR — save SKILL.md to skills/{stage}/{slug}/SKILL.md and open a PR to Affitor/affiliate-skills
2. Share the skill on 2-3 platforms (X, LinkedIn, Reddit r/ChatGPT) with a brief demo of what it does
3. Add the skill to your bio link page — it's a portfolio piece that builds authority
4. Ask 2-3 colleagues to try it and give feedback via GitHub Issues or Discussions

Track Your Results

After 7 days: how many installs and GitHub stars? After 30 days: any community PRs or feedback? If engagement is growing, create more skills in related areas to build a skill portfolio. Your skills become your brand — people trust affiliates who create useful tools.

Next step — copy-paste this prompt: "Find ways to improve my skill based on usage feedback" → runs self-improver

Flywheel Connections

Feeds Into

• skill-finder (S8) — newly created skills appear in the repo for discovery
• self-improver (S8) — skill structure enables automated quality improvement
• compliance-checker (S8) — validates skill outputs against FTC and platform rules
• Any stage-specific skill — new skills expand the flywheel

Fed By

• submit-program (S1) — program listings inspire related skill ideas
• niche-opportunity-finder (S1) — high-opportunity niches reveal skill gaps
• Community requests via GitHub Issues — "I wish there was a skill for X"

Feedback Loop

• Skills with high install counts and GitHub stars reveal which formats and structures resonate most. Low-engagement skills get revised. The Quality Gate evolves based on which checks correlate with high-quality, high-engagement skill output.

chain_metadata:
  skill_slug: "create-skill"
  stage: "meta"
  timestamp: string
  suggested_next:
    - "skill-finder"
    - "self-improver"
    - "compliance-checker"

Quality Gate

Before marking this skill's output as complete:

1. SKILL.md contains all 12 required sections (frontmatter, intro, stage, when-to-use, input schema, workflow, output schema, output format, error handling, examples, flywheel, quality gate)
2. Every workflow step specifies action + approach + quality bar (not just "write the thing")
3. Output Format is a code block with [placeholder] brackets, not prose description
4. At least 2 examples show concrete input → decision → output excerpt
5. Error handling addresses realistic failures (vague input, too simple, too complex)
6. Quality gate items are objectively testable, not subjective ("good quality")
7. The README description is distinct from the SKILL.md content — it's a human-friendly summary
8. Frontmatter name field matches the slug exactly

References

• shared/references/flywheel-connections.md — master flywheel connection map
• shared/references/skill-template.md — canonical SKILL.md template (if exists)